Classic Species-area Relationship Research Articles

Diversity-scaling analysis of human breast milk microbiomes from population perspective.

Quantitative measuring the population-level diversity-scaling of human microbiomes is different from conventional approach to traditional individual-level diversity analysis, and it is of obvious significance. For example, it is well known that individuals are of significant heterogeneity with their microbiome diversities, and the population-level analysis can effectively capture such kind of individual differences. Here we reanalyze a dozen datasets of 2,115 human breast milk microbiome (BMM) samples with diversity-area relationship (DAR) to tackle the previous questions. Our focus on BMM is aimed to offer insights for supplementing the gut microbiome research from nutritional perspective. DAR is an extension to classic species-area relationship, which was discovered in the 19th century and established as one of a handful fundamental laws in community ecology. Our DAR modeling revealed the following numbers, all approximately: (i) The population-level potential diversity of BMM is 1,108 in terms of species richness (number of total species), and 67 in terms of typical species. (ii) On average, an individual carry 17% of population-level diversity in terms of species richness, and 61% in terms of typical species. (iii) The similarity (overlap) between individuals according to pair-wise diversity overlap (PDO) should be approximately 76% in terms of total species, and 92% in terms of typical species, which symbolizes the inter-individual heterogeneity. (iv) The average individual (alpha-) diversity of BMM is approximately 188 (total-species) and 37 (typical-species). (v) To deal with the potential difference among 12 BMM datasets, we conducted DAR modeling separately for each dataset, and then performed permutation tests for DAR parameters. It was found that the DAR scaling parameter that measures inter-individual heterogeneity in diversity is invariant (constant), but the population potential diversity is different among 30% of the pair-wise comparison between 12 BMM datasets. These results offer comprehensive biodiversity analyses of the BMM from host individual, inter-individual, and population level perspectives.

Host-Population Microbial Diversity Scaling of Chinese Gut Microbiomes in Gout Patients.

Gout is a prevalent chronic inflammatory disease that affects the life of tens of millions of people worldwide, and it typically presents as gout arthritis, gout stone, or even kidney damage. Research has revealed its connection with the gut microbiome, although exact mechanism is still unclear. Studies have shown the decline of microbiome diversity in gout patients and change of microbiome compositions between the gout patients and healthy controls. Nevertheless, how diversity changes across host individuals at a cohort (population) level has not been investigated to the best of our knowledge. Here we apply the diversity-area relationship (DAR), which is an extension to the classic SAR (species-area relationship) and establishes the power-function model between microbiome diversity and the number of individuals within cohort, to comparatively investigate diversity scaling (changes) of gut microbiome in gout patients and healthy controls. The DAR modeling with a study involving 83 subjects (41 gout patients) revealed that the potential number of microbial species in gout patients is only 70% of that in the healthy control (2790 vs 3900) although the difference may not be statistically significant. The other DAR parameters including diversity scaling and similarity parameters did not show statistically significant differences. We postulate that the high resilience of gut microbiome may explain the lack of significant gout-disease effects on gut microbial diversity at the population level. The lack of statistically significant difference between the gout patients and healthy controls at host population (cohort) level is different from the previous findings at individual level in the existing literature.

Diversity Scaling of Human Digestive Tract (DT) Microbiomes: The Intra-DT and Inter-individual Patterns.

The human gut microbiome has been extensively studied, but its diversity scaling (changes or heterogeneities) along the digestive tract (DT) as well as their inter-individual heterogeneities have not been adequately addressed to the best of our knowledge. Here we fill the gap by applying the diversity-area relationship (DAR), a recent extension to the classic species-area relationship (SAR) in biogeography, by reanalyzing a dataset of over 2000 16s-rRNA microbiome samples obtained from 10 DT sites of over 200 individuals. We sketched out the biogeography “maps” for each of the 10 DT sites by cross-individual DAR analysis, and the intra-DT distribution pattern by cross-DT-site DAR analysis. Regarding the inter-individual biogeography, it was found that all DT sites have the invariant (constant) scaling parameter—all sites possessing the same diversity change rate across individuals, but most sites have different potential diversities, which include the portions of diversity that may be absent locally but present regionally. In the case of this study, the potential diversity of each DT site covers the total diversity of the respective site from all individuals in the cohort. In terms of the genus richness, an average individual hosts approximately 20% of the population-level genus richness (total bacterial genus of a human population). In contrast, in terms of community biodiversity, the percentages of individual over population may exceed 90%. This suggests that the differences between individuals in their DT microbiomes are predominantly in the composition of bacterial species, rather than how their abundances are distributed (i.e., biodiversity). Regarding the intra-DT patterns, the scaling parameter (z) is larger—suggesting that the intra-DT biodiversity changes are larger than inter-individual changes. The higher intra-DT heterogeneity of bacteria diversity, as suggested by larger intra-DT z than the inter-individual heterogeneity, should be expected since the intra-DT heterogeneity reflects the functional differentiations of the DT tract, while the inter-individual heterogeneity (z) reflects the difference of the same DT site across individuals. On average, each DT site contains 21–36% of the genus diversity of the whole DT, and the percentages are even higher in terms of higher taxon levels.

Population-level diversity-disease relationship (p-DDR) in the human microbiome associated diseases

Diversity-disease relationship (DDR) is a de facto standard analysis in the studies of human microbiome associated diseases (MADs). For example, the species richness or Shannon entropy are routinely compared between the healthy and diseased groups. Nevertheless, the basic scale of the standard diversity analysis is individual subject rather than a cohort or population because the diversity is computed for individual samples, not for the group. Here we aim to expand the current DDR study from individual focus to population level, which can offer important insights for understanding the epidemiology of MADs. We analyzed the diversity-disease relationship at cohort scale based on a collection of 23 datasets covering the major human MADs. Methodologically, we harness the power of a recent extension to the classic species-area relationship (SAR), i.e., the diversity-area relationship (DAR), to achieve the expansion from individual DDR to inter-subject diversity scaling analysis. Specifically, we apply the DAR analysis to estimate and compare the potentially maximal accrual diversities of the healthy and diseases groups, as well as the inter-subject diversity scaling parameters and the individual-to-population diversity ratios. It was shown that, except for the potential diversity (Dmax) at the cohort level in approximately 5.4% cases of MADs, DAR parameters displayed no significant differences between healthy and diseased treatments. That is, the DAR parameters are rather resilient against MADs, except for the potential diversity in some diseases. We compared our population-level DDR with the existing individual-level DDR patterns and proposed a hypothesis to interpret their differences.

Habitat amount, not patch size and isolation, drives species richness of macro‐moth communities in countryside landscapes

AbstractAimOur aim was to test whether species richness patterns are best explained by the effect of the total amount of habitat within the landscape, or instead by a combination of patch size and patch isolation effects. To this end, we jointly contrast the habitat amount hypothesis and countryside biogeography with patch size and isolation concepts from island biogeography.LocationThree multi‐habitat landscapes in Peneda‐Gerês National Park, NW Portugal.TaxonMacro‐moths (Lepidoptera).MethodsLight‐trapping using a semi‐nested design at 84 fixed sites which were each repeatedly sampled six times.ResultsAutocovariate models show that sampling sites with a higher number of forest and meadow macro‐moth species (alpha diversity) were surrounded by a higher amount of forest and meadow habitat, respectively within a 160 and 320 m radius (scale of effect). These top‐ranked models, containing only habitat amount as a significant variable, had lower Akaike's information criteria (AIC) than models (only) containing patch size and/or isolation. Complementary to this, the countryside species–area relationship (SAR) model outperforms the classic SAR model, so that the effective area of habitat explains landscape species richness (gamma diversity) across spatial scales (beta diversity) better than the classic SAR. Specifically, we show that forest macro‐moths have a higher spatial turnover than meadow macro‐moths and that, on average, there are more species in forest than in meadow habitat.Main conclusionsThe habitat amount hypothesis predicts alpha species richness in multi‐habitat landscapes better than do patch size and isolation while the countryside SAR predicts beta and gamma diversity better than the classic SAR. We suggest that evidence is mounting to revise the application of the classical approaches of island biogeography and metapopulation theory to conservation biogeography.

Global Microbiome Diversity Scaling in Hot Springs With DAR (Diversity-Area Relationship) Profiles.

The spatial distribution of biodiversity (i.e., the biogeography) of the hot-spring microbiome is critical for understanding the microbial ecosystems in hot springs. We investigated the microbiome diversity scaling (changes) over space by analyzing the diversity-area relationship (DAR), which is an extension to classic SAR (species-area relationship) law in biogeography. We built DAR models for archaea and bacteria with 16S-rRNA sequencing datasets from 165 hot springs globally. From the DAR models, we sketch out the biogeographic maps of hot-spring microbiomes by constructing: (i) DAR profile—measuring the archaea or bacteria diversity scaling over space (areas); (ii) PDO (pair-wise diversity overlap or similarity) profile—estimating the PDO between two hot springs; (iii) MAD (maximal accrual diversity) profile—predicting the global MAD; (iv) LRD/LGD (ratio of local diversity to regional or global diversity) profile. We further investigated the differences between archaea and bacteria in their biogeographic maps. For example, the comparison of DAR-profile maps revealed that the archaea diversity is more heterogeneous (i.e., more diverse) or scaling faster than the bacterial diversity does in terms of species numbers (species richness), but is less heterogeneous (i.e., less diverse) or scaling slower than bacteria when the diversity (Hill numbers) were weighted in favor of more abundant dominant species. When the diversity is weighted equally in terms of species abundances, archaea, and bacteria are equally heterogeneous over space or scaling at the same rate. Finally, unified DAR models (maps) were built with the combined datasets of archaea and bacteria.

Semen Microbiome Biogeography: An Analysis Based on a Chinese Population Study.

Investigating inter-subject heterogeneity (or spatial distribution) of human semen microbiome diversity is of important significance. Theoretically, the spatial distribution of biodiversity constitutes the core of microbiome biogeography. Practically, the inter-subject heterogeneity is crucial for understanding the normal (healthy) flora of semen microbiotas as well as their possible changes associated with abnormal fertility. In this article, we analyze the scaling (changes) of semen microbiome diversity across individuals with DAR (diversity-area relationship) analysis, a recent extension to classic SAR (species-area relationship) law in biogeography and ecology. Specifically, the unit of “area” is individual subject, and the microbial diversity in seminal fluid of an individual (area) is assessed via metagenomic DNA sequencing technique and measured in the Hill numbers. The DAR models were then fitted to the accrued diversity across different number of individuals (area size). We further tested the difference in DAR parameters among the healthy, subnormal, and abnormal microbiome samples in terms of their fertility status based on a cross-sectional study of a Chinese cohort. Given that no statistically significant differences in the DAR parameters were detected among the three groups, we built unified DAR models for semen microbiome by combining the healthy, subnormal, and abnormal groups. The model parameters were used to (i) estimate the microbiome diversity scaling in a population (cohort), and construct the so-termed DAR profile; (ii) predict/construct the maximal accrual diversity (MAD) profile in a population; (iii) estimate the pair-wise diversity overlap (PDO) between two individuals and construct the PDO profile; (iv) assess the ratio of individual diversity to population (RIP) accrual diversity. The last item (RIP) is a new concept we propose in this study, which is essentially a ratio of local diversity to regional or global diversity (LRD/LGD), applicable to general biodiversity investigation beyond human microbiome.

DAR (diversity-area relationship): Extending classic SAR (species-area relationship) for biodiversity and biogeography analyses.

I extend the classic SAR, which has achieved status of ecological law and plays a critical role in global biodiversity and biogeography analyses, to general DAR (diversity–area relationship). The extension was aimed to remedy a serious application limitation of the traditional SAR that only addressed one aspect of biodiversity scaling—species richness scaling over space, but ignoring species abundance information. The extension was further inspired by a recent consensus that Hill numbers offer the most appropriate measures for alpha‐diversity and multiplicative beta‐diversity. In particular, Hill numbers are essentially a series of Renyi's entropy values weighted differently along the rare‐common‐dominant spectrum of species abundance distribution and are in the units of effective number of species (or species equivalents such as OTUs). I therefore postulate that Hill numbers should follow the same or similar law of the traditional SAR. I test the postulation with the American gut microbiome project (AGP) dataset of 1,473 healthy North American individuals. I further propose three new concepts and develop their statistical estimation formulae based on the new DAR extension, including: (i) DAR profile—z–q relationship (DAR scaling parameter z at different diversity order q), (ii) PDO (pair‐wise diversity overlap) profile—g–q relationship (PDO parameter g at order q, and (iii) MAD (maximal accrual diversity: D max) profile—D max‐q. While the classic SAR is a special case of our new DAR profile, the PDO and MAD profiles offer novel tools for analyzing biodiversity (including alpha‐diversity and beta‐diversity) and biogeography over space.

Sketching the Human Microbiome Biogeography with DAR (Diversity-Area Relationship) Profiles.

SAR (species area relationship) is a classic ecological theory that has been extensively investigated and applied in the studies of global biogeography and biodiversity conservation in macro-ecology. It has also found important applications in microbial ecology in recent years thanks to the breakthroughs in metagenomic sequencing technology. Nevertheless, SAR has a serious limitation for practical applications-ignoring the species abundance and treating all species as equally abundant. This study aims to explore the biogeography discoveries of human microbiome over 18 sites of 5 major microbiome habitats, establish the baseline DAR (diversity-area scaling relationship) parameters, and perform comparisons with the classic SAR. The extension from SAR to DAR by adopting the Hill numbers as diversity measures not only overcomes the previously mentioned flaw of SAR but also allows for obtaining a series of important findings on the human microbiome biodiversity and biogeography. Specifically, two types of DAR models were built, the traditional power law (PL) and power law with exponential cutoff (PLEC), using comprehensive datasets from the HMP (human microbiome project). Furthermore, the biogeography "maps" for 18 human microbiome sites using their DAR profiles for assessing and predicting the diversity scaling across individuals, PDO profiles (pair-wise diversity overlap) for measuring diversity overlap (similarity), and MAD profile (for predicting the maximal accrual diversity in a population) were sketched out. The baseline biogeography maps for the healthy human microbiome diversity can offer guidelines for conserving human microbiome diversity and investigating the health implications of the human microbiome diversity and heterogeneity.

Improving extinction projections across scales and habitats using the countryside species-area relationship

The species-area relationship (SAR) has been often used to project species extinctions as a consequence of habitat loss. However, recent studies have suggested that the SAR may overestimate species extinctions, at least in the short-term. We argue that the main reason for this overestimation is that the classic SAR ignores the persistence of species in human-modified habitats. We use data collected worldwide to analyse what is the fraction of bird and plant species that remain in different human-modified habitats at the local scale after full habitat conversion. We observe that both taxa have consistent responses to the different land-use types, with strongest reductions in species richness in cropland across the globe, and in pasture in the tropics. We show that the results from these studies cannot be linearly scaled from plots to large regions, as this again overestimates the impacts of land-use change on biodiversity. The countryside SAR provides a unifying framework to incorporate both the effect of species persistence in the landscape matrix and the non-linear response of the proportion of species extinctions to sampling area, generating more realistic projections of biodiversity loss.

Species richness, environmental heterogeneity and area: a case study based on land snails in Skyros archipelago (Aegean Sea, Greece)

AbstractAim To test the performance of the choros model in an archipelago using two measures of environmental heterogeneity. The choros model is a simple, easy‐to‐use mathematical relationship which approaches species richness as a combined function of area and environmental heterogeneity.Location The archipelago of Skyros in the central Aegean Sea (Greece).Methods We surveyed land snails on 12 islands of the archipelago. We informed the choros model with habitat data based on natural history information from the land snail species assemblage. We contrast this with habitat information taken from traditional vegetation classification to study the behaviour of choros with different measures of environmental heterogeneity. R2 values and Akaike's information criterion (AIC) were used to compare the choros model and the Arrhenius species–area model. Path analysis was used to evaluate the variance in species richness explained by area and habitat diversity.Results Forty‐two land snail species were recorded, living in 33 different habitat types. The choros model with habitat types had more explanatory power than the classic species–area model and the choros model using vegetation types. This was true for all islands of the archipelago, as well as for the small islands alone. Combined effects of area and habitat diversity primarily explain species richness in the archipelago, but there is a decline when only small islands are considered. The effects of area are very low both for all the islands of the archipelago, and for the small islands alone. The variance explained by habitat diversity is low for the island group as a whole, but significantly increases for the small islands.Main conclusions The choros model is effective in describing species‐richness patterns of land snails in the Skyros Archipelago, incorporating ecologically relevant information on habitat occupancy and area. The choros model is more effective in explaining richness patterns on small islands. When using traditional vegetation types, the choros model performs worse than the classic species–area relationship, indicating that use of proxies for habitat diversity may be problematic. The slopes for choros and Arrhenius models both assert that, for land snails, the Skyros Archipelago is a portion of a larger biogeographical province. The choros model, informed by ecologically relevant habitat measures, in conjunction with path analysis points to the importance of habitat diversity in island species richness.

Area and habitat relationships in island land snail faunas: an Aegean case study exploring the choros model

In the present paper we study the biogeography of land snails in the Skyros archipelago (central Aegean) using both conventional species-area analysis and the newly-developed choros model. The choros model is a simple mathematical relationship, which analyses species richness as a function of area and of environmental heterogeneity, quantified by assessing the number of defined habitats present on each island. We surveyed land snails on 12 islands of the archipelago of Skyros in the central Aegean Sea (Greece). The different types of habitats were defined based on the ecology and biology of each species distributed in the archipelago. Forty-two land snail species were recorded living in 11 different habitat types. The z-values from both models, the choros and the classic species-area relationship, place the archipelago in the within biogeographic province category, mainly due to the recent formation of the archipelago. This recent formation resulted in a group of small islands still behaving as parts of a continuous landmass. The recorded extinction of land snails in the archipelago follows the general pattern of extinctions of this animal group in the Aegean area. The choros model gave a better fit to the data than the classic species-area model, indicating that the range of habitats available plays a significant role in determining faunal diversity independently of area. The availability of knowledge on the ecology of each species in most islands of the Aegean enabled us to recognise the key environmental features on which we based the definition of the habitat types. Such background knowledge is essential if island studies are to progress beyond the simple demonstration of a species! area relationship.